What's the relationship between CNNs and communication systems?
This work provides a novel perspective for understanding CNNs, potentially aiding in explaining neural network research and guiding future directions, though it appears incremental in interpretability methods.
The paper tackles the problem of interpreting Convolutional Neural Networks (CNNs) by comparing them to communication systems, establishing a correspondence between their modules and validating it experimentally.
The interpretability of Convolutional Neural Networks (CNNs) is an important topic in the field of computer vision. In recent years, works in this field generally adopt a mature model to reveal the internal mechanism of CNNs, helping to understand CNNs thoroughly. In this paper, we argue the working mechanism of CNNs can be revealed through a totally different interpretation, by comparing the communication systems and CNNs. This paper successfully obtained the corresponding relationship between the modules of the two, and verified the rationality of the corresponding relationship with experiments. Finally, through the analysis of some cutting-edge research on neural networks, we find the inherent relation between these two tasks can be of help in explaining these researches reasonably, as well as helping us discover the correct research direction of neural networks.